An efficient treatment against a COVID-19 disease, caused by the novel coronavirus SARS-CoV-2 (CoV2), remains a challenge. The papain-like protease (PL^pro) from the human coronavirus is a protease that plays a critical role in virus replication. Moreover, CoV2 uses this enzyme to modulate the host’s immune system to its own benefit. Therefore, it represents a highly promising target for the development of antiviral drugs. We used Approximate Bayesian Computation tools, molecular modelling and enzyme activity studies to identify highly active inhibitors of the PL^pro. We discovered organoselenium compounds, ebselen and its structural analogues, as a novel approach for inhibiting the activity of PL^proCoV2. Furthermore, we identified, for the first time, inhibitors of PL^proCoV2 showing potency in the nanomolar range. Moreover, we found a difference between PL^pro from SARS and CoV2 that can be correlated with the diverse dynamics of their replication, and, putatively to disease progression.

Seven crystal structures of alanyl aminopeptidase from Neisseria meningitides (the etiological agent of meningitis, NmAPN) complexed with organophosphorus compounds were resolved to determine the optimal inhibitor–enzyme interactions. The enantiomeric phosphonic acid analogs of Leu and hPhe, which correspond to the P1 amino acid residues of well-processed substrates, were used to assess the impact of the absolute configuration and the stereospecific hydrogen bond network formed between the aminophosphonate polar head and the active site residues on the binding affinity. For the hPhe analog, an imperfect stereochemical complementarity could be overcome by incorporating an appropriate P1 side chain. The constitution of P1′-extended structures was rationally designed and the lead, phosphinic dipeptide hPhePψ[CH₂]Phe, was modified in a single position. Introducing a heteroatom/heteroatom-based fragment to either the P1 or P1′ residue required new synthetic pathways. The compounds in the refined structure were low nanomolar and subnanomolar inhibitors of N. meningitides, porcine and human APNs, and the reference leucine aminopeptidase (LAP). The unnatural phosphinic dipeptide analogs exhibited a high affinity for monozinc APNs associated with a reasonable selectivity versus dizinc LAP. Another set of crystal structures containing the NmAPN dipeptide ligand were used to verify and to confirm the predicted binding modes; furthermore, novel contacts, which were promising for inhibitor development, were identified, including a π–π stacking interaction between a pyridine ring and Tyr372.

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).

Since December 2019 a novel a coronavirus identified as SARS-CoV-2 or COV2 has been spreading around the world. On the 16th of May around 4.5 million people got infected and over 300,000 died due to the infection of COV2. The effective treatment remains a challenge. Targeted therapeutics are still under investigation. The papain-like protease (PL ^Pro ) from the human SARS-CoV-2 coronavirus is a cysteine protease that plays a critical role in virus replication. Its activity is required to process the viral polyprotein into functional, mature subunits. Moreover, COV2 uses this enzyme to modulate the host’s immune system to its own benefit. Therefore, it represents a highly promising target for the development of antiviral drugs. In this work, we discovered that ebselen, a synthetic organoselenium drug molecule with anti-inflammatory, anti-oxidant and cytoprotective activity in mammalian cells and cytotoxicity in lower organisms, is a highly active inhibitor of PL ^Pro CoV2. We proved that ebselen is a covalent, fast-binding inhibitor of PL ^Pro CoV2 exhibiting a low micromolar potency. Furthermore, we identified a difference between PL ^Pro from SARS-CoV-1 (the corona virus which caused the 2002–2004 outbreak, SARS) and SARS-CoV-2 that allows to explain the difference in dynamics of the replication, and, thus, the disease progression. Namely, we present that they show differences in the binding affinity of substrates that we observed through kinetics and molecular docking studies. Using a novel Approximate Bayesian Computation method we were able to find kinetic constants for both enzymes. Molecular modeling study on the structure of the active site and binding mode of the ebselen with SARS and COV2 showed also significant differences that could explain our observation that ebselen is less active and slower bounding with SARS than COV2. In conclusion, we show that ebselen inhibits the activity of the essential viral enzyme papain-like protease (PLpro) from SARS-COV-2 in low micromolar range.